报告题目：Dynamic Weighting for Synthetic Control Method
报告人：洪少新 山东大学
报告时间：2023年11月7日(周二) 16:30-18:00
报告地点：中国科学院数学与系统科学研究院南楼N204
腾讯会议ID：393 3774 3329
内容摘要
We propose a novel synthetic control method with a dynamic weighting scheme to evaluate the impacts of social policy. The basic idea is to utilize the interdependence between different control units in a panel dataset to create the counterfactuals locally. Unlike the existing literature, we allow the weights to change over state variables and thus it is expected to capture potentially nonlinear features in economics and finance. It is shown that the treatment-effect estimator is asymptotically optimal in the sense of achieving the lowest possible local squared prediction error. The rate of the selected weights converging to the optimal weights to minimizing the expected local quadratic loss is established. Simulations and empirical applications are conducted to evaluate the finite sample performance of the proposed method.
主讲人简介
洪少新，山东大学经济研究院助理研究员。2018年取得美国北卡罗来纳大学夏洛特分校统计学博士学位。主要从事计量经济学和统计学的理论和应用研究工作，具体研究方向包括非平稳时间序列分析、模型平均方法和高维数据统计推断等。主要工作发表于Econometrics Journal， Economics Letters和Journal of Financial Econometrics等。主持国家自然科学基金青年项目。
